CVE-2024-37053 MLFlow unsafe deserialization
Attackers can execute arbitrary code on user systems by uploading malicious scikit-learn models to the MLflow platform.
- Exploited in the wild
- Not known
- Login needed
- Not needed
- Attackable
- Over the internet
- Victim action needed
- A user has to click or open something
- Fix
- Available
What is CVE-2024-37053?
CVE-2024-37053 is a high-severity unsafe deserialization vulnerability in Lfprojects Mlflow, published on 4 Jun 2024. An attacker can exploit it over the internet without logging in, and only if a user opens a file or follows a link.
Official description
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.
Weakness: CWE-502
Which versions are affected by CVE-2024-37053?
4 open source packages ship an affected version, including mlflow. It affects 1 product.
| Package | Affected | Fixed in |
|---|---|---|
mlflow Bitnami | From 1.1.0 | 2.14.2 |
mlflow PyPI | From 1.1.0 to 2.14.1 | None |
mlflow PyPI | From 1.1.0 to 2.14.1 | None |
mlflow PyPI | From 1.1.0 to 2.14.1 | None |
Products include Lfprojects Mlflow.
How do I fix CVE-2024-37053?
Upgrade mlflow to 2.14.2 or later.
Upgrade, and only deserialise data from trusted sources or with an allow-list of types. More about Unsafe deserialization.
Is CVE-2024-37053 being exploited?
There is no known exploitation of CVE-2024-37053 in the wild and no public exploit code.
Other vulnerabilities in Lfprojects Mlflow
- CVE-2026-64849 MLflow Server-Side Request Forgery
- CVE-2025-15379 Command Injection in mlflow/mlflow
- CVE-2024-0520 Remote Code Execution due to Full Controlled File Write in mlflow/mlflow
- CVE-2023-6977 Path Traversal: '\..\filename'
- CVE-2023-6709 Improper Neutralization of Special Elements Used in a Template Engine in mlflow/mlflow
Sources
Also known as BIT-mlflow-2024-37053, GHSA-43c4-9qgj-x742, PYSEC-2026-1636.